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Abstract
Image inpainting is the process of filling in missing parts of damaged images based on information gleaned from surrounding areas. In this paper, we present two variational models for image inpainting. Combining two models, we can simultaneously fill in missing, corrupted or undesirable information, while remove noise. We explain that diffusion performance of the proposed models is essentially superior to that of TV inpainting model by analysing the physical characteristics in local coordinates, and investigate the existence of minimising functionals in BV space. The experimental results show the effective performance of the proposed models in restoring scratched photos, text removal, and even removal of entire objects from images.
The authors would like to thank National Nature Science Foundation of China (No.10971234), Tianyuan Foundation of National Nature Science Foundation of China (No.11026227), Fundamental Research Funds for the Central Universities (No.10lgzd09), and Technology Plan Funds of Guangzhou (No.2010C6-I00011).